用贝叶斯自助法提升聚类稳定性与可解释性，自动判断最优聚类数。
A Bayesian Approach to Clustering via the Proper Bayesian Bootstrap: the Bayesian Bagged Clustering (BBC) algorithm
- 先用k-means获取先验，再用贝叶斯自助法进行集成聚类。
- 通过香农熵衡量不确定性，清晰指示最优聚类数量。
- 适合追求聚类结果可信度和自动选簇数的研究者。
本文提出一种新颖的无监督聚类方法，通过引入恰当的贝叶斯自助法（proper Bayesian bootstrap）改进现有模型在鲁棒性和可解释性方面的表现。方法分两步：首先使用k-means进行先验信息提取，然后将贝叶斯自助法作为重采样策略应用于集成聚类框架。通过香农熵构建不确定性度量，分析结果能够清晰指示最优聚类数，并提供更准确的数据聚类表示。在模拟数据上的实证结果展示了该方法在方法论和实际性能上的显著提升。
原文摘要 · Abstract (English)
The paper presents a novel approach for unsupervised techniques in the field of clustering. A new method is proposed to enhance existing literature models using the proper Bayesian bootstrap to improve results in terms of robustness and interpretability. Our approach is organized in two steps: k-means clustering is used for prior elicitation, then proper Bayesian bootstrap is applied as resampling method in an ensemble clustering approach. Results are analyzed introducing measures of uncertainty based on Shannon entropy. The proposal provides clear indication on the optimal number of clusters, as well as a better representation of the clustered data. Empirical results are provided on simulated data showing the methodological and empirical advances obtained.
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